1. Predicting the Future With Social Media

4. Introduction Goal – How buzz and attention is created for different movies and how that changes over time – How sentiments are created, how that propagates and how they influence people Hypothesis – Well-talked movies will be well-watched

5. Prior Work Using meta-data information of movie – Genre – MPAA rating – Running time – Release date – Number of screen – Actor – Director – Etc W. Zhang and S. Skiena. “Improving movie gross prediction through news analysis.” In Web Intelligence, 2009 – Used a news aggregation model along with IMDB data

6. Dataset Characteristics 2.89 million tweets / 24 different movies / 3 months Critical Period release 1 week 2 week

7. Dataset Characteristics

11. Attention and Popularity Prior to the release of a movie – Expect the tweets to consist promotional campaign – Tweets and retweets referring to particular urls(photo, trailers, …)

12. Attention and Popularity

13. Correlation between urls and retweets with the box-office revenues Tweet-rate

14. Attention and Popularity Correlation between avg tweet-rate and BO revenues = 0.9 Strong linear relationship => linear regression model Prediction of first weekend Box-office revenues

15. Attention and Popularity Comparison with Hollywood Stock Exchange

16. Attention and Popularity Prediction revenues for a given weekend – Using Tweet-rate timeseries + thcnt

17. Sentiment Analysis Tweets are classified into Positive, Negative or Neutral

18. Sentiment Analysis

20. Attention and Popularity Prediction revenues for a second weekend

21. Conclusion Social media feeds can be effective indicators of real- world performance Tweet-rates can be used to build a powerful model for predicting movie box-office revenue Sentiment in tweets can improve box-office revenue prediction after the movies are released

22. END